e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Using geostatistical and remote sensing approaches for mapping soil properties


This paper attempts to compare various prediction methods for mapping soil properties (texture, organic matter (OM), pH, phosphor-us and potassium) for precision farming approaches by incorporating secondary spatial information into the mapping. The primary information (or primary attribute) was obtained from an intensive grid soil sampling and the secondary spatial information from digital (or spectral) data from an aerial colour photograph of bare soil. The prediction methods were statistical (linear regression between soil properties and digital values) and geostatistical algorithms (ordinary kriging, ordinary kriging plus regression and kriging with varying local means). Mean square error (MSE) was used to evaluate the performance of the map prediction quality. The best prediction method for mapping organic matter, pH and potassium was kriging with varying local means in combination with the spectral data from the blue waveband with the smallest MSE indicating the highest precision. Maps from these kriged estimates showed that a combination of geostatistical techniques and digital data from aerial photograph could improve the prediction quality of soil management zones, which is the first step for site-specific soil management. (c) 2005 Elsevier B.V. All rights reserved.

  • ES
  • CSIC_Spanish_Natl_Res_Council (ES)
Data keywords
  • digital data
Agriculture keywords
  • farming
Data topic
  • sensors
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • CSIC_Spanish_Natl_Res_Council (ES)
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e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.